Posted in Asia-Pacific, Research

KGDCMI: A New Approach for Predicting circRNA-miRNA Interactions From Multi-Source Information Extraction and Deep Learning

 September 4, 2022



doi: 10.3389/fgene.2022.958096.


eCollection 2022.

Affiliations

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Xin-Fei Wang et al.


Front Genet.


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Abstract

Emerging evidence has revealed that circular RNA (circRNA) is widely distributed in mammalian cells and functions as microRNA (miRNA) sponges involved in transcriptional and posttranscriptional regulation of gene expression. Recognizing the circRNA-miRNA interaction provides a new perspective for the detection and treatment of human complex diseases. Compared with the traditional biological experimental methods used to predict the association of molecules, which are limited to the small-scale and are time-consuming and laborious, computing models can provide a basis for biological experiments at low cost. Considering that the proposed calculation model is limited, it is necessary to develop an effective computational method to predict the circRNA-miRNA interaction. This study thus proposed a novel computing method, named KGDCMI, to predict the interactions between circRNA and miRNA based on multi-source information extraction and fusion. The KGDCMI obtains RNA attribute information from sequence and similarity, capturing the behavior information in RNA association through a graph-embedding algorithm. Then, the obtained feature vector is extracted further by principal component analysis and sent to the deep neural network for information fusion and prediction. At last, KGDCMI obtains the prediction accuracy (area under the curve [AUC] = 89.30% and area under the precision-recall curve [AUPR] = 87.67%). Meanwhile, with the same dataset, KGDCMI is 2.37% and 3.08%, respectively, higher than the only existing model, and we conducted three groups of comparative experiments, obtaining the best classification strategy, feature extraction parameters, and dimensions. In addition, in the performed case study, 7 of the top 10 interaction pairs were confirmed in PubMed. These results suggest that KGDCMI is a feasible and useful method to predict the circRNA-miRNA interaction and can act as a reliable candidate for related RNA biological experiments.


Keywords:

K-mer; circRNA; circRNA–miRNA interaction; deep neural network; graph embedding.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures


FIGURE 1



FIGURE 1

The K-mer algorithm for sequence feature extraction.


FIGURE 2



FIGURE 2

Flowchart of KGDCMI.


FIGURE 3



FIGURE 3

Receiver operating characteristic curves generated by KGDCMI.


FIGURE 4



FIGURE 4

Area under the precision–recall curves generated by KGDCMI.


FIGURE 5



FIGURE 5

Performance comparison of five traditional classifiers and DNN in terms of prediction.


FIGURE 6



FIGURE 6

Performances of different K values.


FIGURE 7



FIGURE 7

Performance of five-dimensional compression to extract features.

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